Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm
A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods...
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MDPI AG
2023-07-01
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Series: | Bioengineering |
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Online Access: | https://www.mdpi.com/2306-5354/10/8/909 |
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author | Jihao Zhai Junzhong Ji Jinduo Liu |
author_facet | Jihao Zhai Junzhong Ji Jinduo Liu |
author_sort | Jihao Zhai |
collection | DOAJ |
description | A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data. |
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language | English |
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publishDate | 2023-07-01 |
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series | Bioengineering |
spelling | doaj.art-e56127256d894eab9e99dcead82245722023-11-19T00:17:43ZengMDPI AGBioengineering2306-53542023-07-0110890910.3390/bioengineering10080909Learning Causal Biological Networks with Parallel Ant Colony Optimization AlgorithmJihao Zhai0Junzhong Ji1Jinduo Liu2Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaBeijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaBeijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaA wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data.https://www.mdpi.com/2306-5354/10/8/909causal biological networkscausal brain networkscausal protein signaling networksparallel ant colony optimizationpheromone fusionCBNs fusion |
spellingShingle | Jihao Zhai Junzhong Ji Jinduo Liu Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm Bioengineering causal biological networks causal brain networks causal protein signaling networks parallel ant colony optimization pheromone fusion CBNs fusion |
title | Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm |
title_full | Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm |
title_fullStr | Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm |
title_full_unstemmed | Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm |
title_short | Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm |
title_sort | learning causal biological networks with parallel ant colony optimization algorithm |
topic | causal biological networks causal brain networks causal protein signaling networks parallel ant colony optimization pheromone fusion CBNs fusion |
url | https://www.mdpi.com/2306-5354/10/8/909 |
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